Self-organization of complex networks as a dynamical system

Emergence of food webs with a multi-trophic hierarchical structure driven by nonlinear trait-matching

Food webs are a central subject in community ecology, because consumption supports the flow of matter through the system, which is at the base of many of its functions. Identifying the mechanisms that are at the origin of food web structure is useful, e.g., for restoration purposes. We investigated the extent to which trait-matching, which contributes to defining the strength of trophic interactions, can cause the emergence of food webs with a non-trivial, multi-trophic, hierarchical structure. We compared for that purpose the structural properties of food webs simulated by four food web model variants, depending whether trait-matching was linear or nonlinear and whether population dynamics and evolution were accounted for (dynamical model) or not (static model). Nonlinear trait-matching can restrict interactions in phenotypic space so as to obtain localized interactions (i.e., each species interact with a small subset of species), which is a key element for food web formation. In the static case, nonlinear trait-matching allowed for the emergence of food webs, at a relatively low connectance as with random graphs. In the dynamical case, nonlinear trait-matching combined with population dynamics and evolution allowed for the formation of groups of phenotypically close species, resulting in food webs with a multi-trophic, hierarchical structure.

A model for simulating emergent patterns of cities and roads on real-world landscapes

Emergence of cities and road networks have characterised human activity and movement over millennia. However, this anthropogenic infrastructure does not develop in isolation, but is deeply embedded in the natural landscape, which strongly influences the resultant spatial patterns. Nevertheless, the precise impact that landscape has on the location, size and connectivity of cities is a long-standing, unresolved problem. To address this issue, we incorporate high-resolution topographic maps into a Turing-like pattern forming system, in which local reinforcement rules result in co-evolving centres of population and transport networks. Using Italy as a case study, we show that the model constrained solely by topography results in an emergent spatial pattern that is consistent with Zipf’s Law and comparable to the census data. Thus, we infer the natural landscape may play a dominant role in establishing the baseline macro-scale population pattern, that is then modified by higher-level historical, socio-economic or cultural factors.